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hadiparsianNIHhadiparsianNIH
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updated cloud formation templete link
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‎AWS/01-RNA-Seq/RNA-seq.ipynb

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"cell_type": "code",
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"execution_count": null,
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"id": "f0e9ac26-e308-471a-83a4-113f767524b0",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"IRdisplay::display_html('<iframe src = \"../../quiz_files/rna-pre_module.html\" width=95% height=600></iframe>')"
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"cell_type": "code",
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"execution_count": null,
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"id": "95cda0d2",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"system('chmod +x install_rna_seq_packages.sh' , intern=TRUE)\n",
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"\n",
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"If you prefer to skip manual deployment and deploy automatically in the cloud, click the Launch Stack button below. For a walkthrough of the screens during automatic deployment please click [here](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToLaunchAWSBatch.md). The deployment should take ~5 min and then the resources will be ready for use. \n",
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"\n",
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"[![Launch Stack](../../images/LaunchStack.jpg)](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/create?stackName=aws-batch-nigms&templateURL=https://nigms-sandbox.s3.us-east-1.amazonaws.com/cf-templates/AWSBatch_template.yaml)\n",
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"[![Launch Stack](../../images/LaunchStack.jpg)](https://console.aws.amazon.com/cloudformation/home?#/stacks/new?stackName=aws-batch-nigms&templateURL=https://nigms-sandbox.s3.us-east-1.amazonaws.com/cf-templates/AWSBatch_template.yaml)\n",
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"\n",
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"\n",
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"Before begining this tutorial, if you do not have required roles, policies, permissions or compute environment and would like to **manually** set those up please click [here](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/AWS-Batch-Setup.md) to set that up."
@@ -245,7 +253,11 @@
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"cell_type": "code",
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"execution_count": null,
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"id": "6da16610-a83f-42b5-be52-f8116db3fb03",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"#Install nexflow, make it exceutable, and update it\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "bb2e1f61-eea1-4464-9842-76fabae4c39a",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"system('./nextflow run nf-core/rnaseq -c rnaseq-aws.config -profile test,aws', intern=TRUE)"
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"cell_type": "code",
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"execution_count": null,
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"id": "a5ea70df-98ba-4a69-aeec-2baed34a72fc",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"library(DESeq2)\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "e49c7007-61b3-4057-89de-3f3fb1085800",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# download data files from storage bucket\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "5fce0d5b-740b-4f9c-8479-fa60a0566532",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"DESeq.ds <- DESeqDataSetFromMatrix(countData = round(readcounts), colData = sample_info, design = ~condition)\n"
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"cell_type": "code",
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"execution_count": null,
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"id": "cb7c8095-8d5c-4165-a0bf-9acfec7d97fe",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"colData(DESeq.ds) %>% head\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "4fc2ab39-8608-435b-a8f9-fb6f379be65a",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"DESeq.ds <- DESeq.ds[ rowSums(counts(DESeq.ds)) > 0, ]\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "40aa5366-4c12-4443-99a9-39ac707639d3",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# Get the size factor using estimateSizeFactors from DESeq.\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "38523c0a-b9af-49ca-b7e8-2dbb4604fe5c",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# transform size-factor normalized read counts to log2 scale using pseudocount of 1\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "a283f6d7-3370-41e7-a85c-c54c695f721f",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# mean-sd plot\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "8f444ca6-b525-4e2e-9f9e-4363663b45fc",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# Regularized log-transformed values\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "8ca292e8-27a2-4336-b047-07751cef499d",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# cor() calculates the correlation between columns of a matrix\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "b38f5fab-4b48-4e74-9e23-793e202312dc",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"P <- plotPCA(DESeq.rlog)\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "f36513db-22f6-417f-aaa8-39ba1837ca35",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"# DESeq2 uses the levels of the condition to determine the order of the comparison\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "17827bd4-0938-49e8-9f3a-b4e979fedb3a",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"#Check the results of deseq analysis\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "39a986b0-3ee2-436a-927b-37f539e7c627",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"#Histogram\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "079404d0-d179-4bdf-8a2a-63e135b68c45",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"#MA plot\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "5bdda266-08e2-47fd-bdd8-f06cd4dd7f0c",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"#HEATMAP\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "38f62bcf-3150-417e-bebb-787763db04aa",
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"metadata": {},
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"metadata": {
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"source": [
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"write.table(DGE.results.sorted, file=\"rna-seq_dge-results.txt\", sep = \"\\t\")"

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